Abstract
In this paper we describe an approach to classifying heart sounds (classes Normal, Murmur and Extra-systole) that is based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The ability of automatically classifying heart sounds or at least support human decision in this task is socially relevant to spread the reach of medical care using simple mobile devices or digital stethoscopes. In our approach, sounds are first pre-processed using signal processing techniques (decimate, low-pass filter, normalize, Shannon envelope). Then the pre-processed symbols are transformed into sequences of discrete SAX symbols. These sequences are subject to a process of motif discovery. Frequent sequences of symbols (motifs) are adopted as features. Each sound is then characterized by the frequent motifs that occur in it and their respective frequency. This is similar to the term frequency (TF) model used in text mining. In this paper we compare the TF model with the application of the TFIDF (Term frequency - Inverse Document Frequency) and the use of bi-grams (frequent size two sequences of motifs). Results show the ability of the motifs based TF approach to separate classes and the relative value of the TFIDF and the bi-grams variants. The separation of the Extra-systole class is overly difficult and much better results are obtained for separating the Murmur class. Empirical validation is conducted using real data collected in noisy environments. We have also assessed the cost-reduction potential of the proposed methods by considering a fixed cost model and using a cost sensitive meta algorithm.

Abstract
In this work, we propose a compact sensor head to perform cryogenic temperature measurements based on a long-period fiber grating. The presented configuration enables the sensor to be interrogated in reflection since a phase-shifted is produced by Fresnel reflection on the end-face of the fiber, cleaved at a quarter-period separation distance from the end of the grating.

Abstract
In order to better understand the musical properties which elicit an increased sensation of wanting to move when listening to music groove we investigate the effect of adding syncopation to simple piano melodies, under the hypothesis that syncopation is correlated to groove. Across two experiments we examine listeners' experience of groove to synthesized musical stimuli covering a range of syncopation levels and densities of musical events, according to formal rules implemented by a computer algorithm that shifts musical events from strong to weak metrical positions. Results indicate that moderate levels of syncopation lead to significantly higher groove ratings than melodies without any syncopation or with maximum possible syncopation. A comparison between the various transformations and the way they were rated shows that there is no simple relation between syncopation magnitude and groove.

Abstract
Self-adaptive software systems are known to respond at run time to changes detected internally or in their environment, in an attempt to keep meeting their own functional requirements and agreed levels of service. Such response usually targets their architectures and involve, in particular, the possibility of their dynamic reconfiguration. In contexts where change is the rule rather than the exception, it is difficult to predict when exactly such reconfigurations are needed, and if they will lead the system into a suitable configuration. However, knowing the main attributes of the context, it is possible to plan configurations that will be more likely to perform well in some conjugation of values for such attributes. In this paper we discuss both a model that lays down reconfiguration strategies, planned at design time, and a strategy which actively uses such a model to trigger architectural adaptations at run time. This strategy builds on a framework intended to the formal verification of architectural requirements, either from a qualitative or quantitative (probabilistic) perspective.

Abstract

Abstract
The Monte Carlo method has proved to be very powerful to cope with global illumination problems but it remains costly in terms of sampling operations. In various applications, previous work has shown that Bayesian Monte Carlo can significantly outperform importance sampling Monte Carlo thanks to a more effective use of the prior knowledge and of the information brought by the samples set. These good results have been confirmed in the context of global illumination but strictly limited to the perfect diffuse case. Our main goal in this paper is to propose a more general Bayesian Monte Carlo solution that allows dealing with nondiffuse BRDFs thanks to a spherical Gaussian-based framework. We also propose a fast hyperparameters determination method that avoids learning the hyperparameters for each BRDF. These contributions represent two major steps toward generalizing Bayesian Monte Carlo for global illumination rendering. We show that we achieve substantial quality improvements over importance sampling at comparable computational cost.